Determining sucrose and glucose levels in dual‐purpose sorghum stalks by Fourier transform near infrared (<scp>FT‐NIR</scp>) spectroscopy

Chen, Shih‐Fang; Danao, Mary‐Grace C.; Brown, Patrick J.

doi:10.1002/jsfa.6606

Cited by 30 publications

(30 citation statements)

References 43 publications

(65 reference statements)

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“…; Chen et al . ). This study presents a successful application of a NIRS‐based NSC quantification considering many woody species, different tissue types and a broad range of environmental conditions.…”

Section: Discussionmentioning

confidence: 97%

“…Conversely, Chen et al . () reported r 2 = 0·81 and RMSE = 1·77% in external validation of models for estimating sucrose and glucose concentration in sorghum stalks. Relative to other studies, the robustness of our models in the independent validation (as compared to the external validation) provides strong support for using our calibration models to estimate NSC concentration of plant species across broad environmental gradients and life histories.…”

Section: Discussionmentioning

confidence: 99%

“…To date, estimating carbohydrate concentrations for plant tissues using NIRS has been restricted to a handful of studies that have focused on annual plants in a single biome (Batten et al 1993;Decruyenaere et al 2012;Chen et al 2014). This study presents a successful application of a NIRS-based NSC quantification considering many woody species, different tissue types and a broad range of environmental conditions.…”

Section: Discussionmentioning

confidence: 99%

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Near‐infrared spectroscopy (NIRS) predicts non‐structural carbohydrate concentrations in different tissue types of a broad range of tree species

et al. 2015

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Summary1. The allocation of non-structural carbohydrates (NSCs) to reserves constitutes an important physiological mechanism associated with tree growth and survival. However, procedures for measuring NSC in plant tissue are expensive and time-consuming. Near-infrared spectroscopy (NIRS) is a high-throughput technology that has the potential to infer the concentration of organic constituents for a large number of samples in a rapid and inexpensive way based on empirical calibrations with chemical analysis. 2.The main objectives of this study were (i) to develop a general NSC concentration calibration that integrates various forms of variation such as tree species and tissue types and (ii) to identify characteristic spectral regions associated with NSC molecules. In total, 180 samples from different tree organs (root, stem, branch, leaf) belonging to 73 tree species from tropical and temperate biomes were analysed. Statistical relationships between NSC concentration and NIRS spectra were assessed using partial least squares regression (PLSR) and a variable selection procedure (competitive adaptive reweighted sampling, CARS), in order to identify key wavelengths. 3. Parsimonious and accurate calibration models were obtained for total NSC (r 2 of 0Á91, RMSE of 1Á34% in external validation), followed by starch (r 2 = 0Á85 and RMSE = 1Á20%) and sugars (r 2 = 0Á82 and RMSE = 1Á10%). Key wavelengths coincided among these models and were mainly located in the 1740-1800, 2100-2300 and 2410-2490 nm spectral regions. 4. This study demonstrates the ability of general calibration model to infer NSC concentrations across species and tissue types in a rapid and cost-effective way. The estimation of NSC in plants using NIRS therefore serves as a tool for functional biodiversity research, in particular for the study of the growth-survival trade-off and its implications in response to changing environmental conditions, including growth limitation and mortality.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Near‐infrared spectroscopy (NIRS) predicts non‐structural carbohydrate concentrations in different tissue types of a broad range of tree species

et al. 2015

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“…However, and particularly to specific sugar content, NIRS in combination with PLSR models has been used in sorghum stalks [40] and sweet sorghum (cellulose, lignin, and hemicellulose) [41], fruit juices [42,43], rice (amylose) [44], whey (lactose) [45], grasses (fructans) [46,47], maize (nonstructural and water soluble carbohydrates) [48], intact apple fruit to determinate fructose, glucose, and sucrose [49], orange [50], apricot [51], sugar beet [52], cherries [53], and other fruits ( Table 2). All these studies accorded that the performance of NIR spectroscopy is comparable to the reference chromatographic method, but the former is much faster and easier to carry out.…”

Section: Applications Of Carbohydrates Analysis By Nirsmentioning

confidence: 99%

Carbohydrate Analysis by NIRS-Chemometrics

López¹,

García-González²,

Franco‐Robles³

2017

Developments in Near-Infrared Spectroscopy

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Near-infrared spectroscopy (NIRS) is a high-throughput, low-cost, solvent-free, and nondestructive analytical tool. Chemometrics is the science that employs statistical and mathematical methods to explain near-infrared spectra; it has been proven that when they are coupled, their effectiveness highly improved in-depth carbohydrate characterization. This chapter focuses on the fundamentals of near-infrared spectroscopy in the study of carbohydrates, as well as the application of partial least squares regression (PLSR) and principal component analysis (PCA), as the most useful chemometric techniques involved in carbohydrate analysis. The theoretical aspects and practical applications starting from simple to complex carbohydrates mixtures are covered. Indeed, the contributions from different fields extend the implementation of near-infrared spectroscopy from industrial quality control to scientific research.

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“…As a C4 plant, it shows an impressive absorption of CO2 during a fast growth in 4-5 months. The US, Brazil, Germany, India, and China have already produced ethanol from sweet sorghum [5], and this plant is one of the three largest crops in US [25,26]. Its cultivation requires only 1/2 and 1/3 the water demand of maize and sugarcane, respectively, and lesser fertilizers.…”

Section: Introductionmentioning

confidence: 99%

Influence of Sowing Times, Densities, and Soils to Biomass and Ethanol Yield of Sweet Sorghum

Xuan

Phuong²,

Khang³

et al. 2015

Sustainability

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Abstract:The use of biofuels helps to reduce the dependency on fossil fuels and therefore decreases CO2 emission. Ethanol mixed with gasoline in mandatory percentages has been used in many countries. However, production of ethanol mainly depends on food crops, commonly associated with problems such as governmental policies and social controversies. Sweet sorghum (Sorghum bicolor (L.) Moench) is one of the most potential and appropriate alternative crops for biofuel production because of its high biomass and sugar content, strong tolerance to environmental stress conditions and diseases, and wide adaptability to various soils and climates. The aim of this study was to select prospective varieties of sweet sorghum, optimum sowing times and densities to achieve high yields of ethanol production and to establish stable operational conditions in cultivating this crop. The summer-autumn cropping season combined with the sowing densities of 8.3-10.9 plant m −2

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Determining sucrose and glucose levels in dual‐purpose sorghum stalks by Fourier transform near infrared (FT‐NIR) spectroscopy

Abstract: PLSR models were developed based on FT-NIR spectra coupled with multivariate data analysis to provide a quick and low-cost estimate of specific sugar contents in grain sorghum stalks. This sugar information helps decision making for sorghum-based biomass processing and storage strategies.

Cited by 30 publications

References 43 publications

Near‐infrared spectroscopy (NIRS) predicts non‐structural carbohydrate concentrations in different tissue types of a broad range of tree species

Near‐infrared spectroscopy (NIRS) predicts non‐structural carbohydrate concentrations in different tissue types of a broad range of tree species

Carbohydrate Analysis by NIRS-Chemometrics

Influence of Sowing Times, Densities, and Soils to Biomass and Ethanol Yield of Sweet Sorghum

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